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    <article class="post-wrap">
        <header class="post-header">
            <h1 class="post-title">基于全卷积网络的SAR目标分类(EOC、SOC)</h1>
            
                <div class="post-meta">
                    
                        Author: <a itemprop="author" rel="author" href="/about/">WD</a>
                     &nbsp;

                    
                        <span class="post-time">
                        Date: <a href="#">August 22, 2021&nbsp;&nbsp;10:07:58</a>
                        </span>
                     &nbsp;
                    
                        <span class="post-category">
                    Category:
                            
                                <a href="/categories/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B/">目标检测</a>
                            
                        </span>
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        <div class="post-content">
            <h2 id="1-MSTAR数据集扩充"><a href="#1-MSTAR数据集扩充" class="headerlink" title="1. MSTAR数据集扩充"></a>1. MSTAR数据集扩充</h2><ul>
<li>在原始SAR图像（128×128）中随机裁剪88×88大小的切片，每一张切片都能包含目标区域，经过随机采样样本数量可达（128-88+1）×（128-88+1）=1681 倍，每一个类别的每张SAR图像都采样10个切片，从而提高训练样本数量。</li>
</ul>
<h2 id="2-全卷积网络模型（AConvNets）"><a href="#2-全卷积网络模型（AConvNets）" class="headerlink" title="2. 全卷积网络模型（AConvNets）"></a>2. 全卷积网络模型（AConvNets）</h2><ul>
<li><p>网络完全由卷积层实现，去掉全连接层，最后一层卷积输出直接Softmax输出每个类别概率，损失函数依旧为分类交叉熵损失，具体网络模型如下：</p>
<p><img src="https://img-blog.csdnimg.cn/895862408c13421f80c157ff3fcc42c5.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMTgxNTky,size_16,color_FFFFFF,t_70#pic_center" alt="image-20210809102008276" style="zoom: 67%;" /></p>
</li>
<li><p>其中卷积层使用BN层防止过拟合并加速训练，输入训练集维度：[b, 88, 88, 1]  -&gt; 输出：[b, 1, 1, 10]，训练仍然采用的分类交叉熵损失，优化器Adam，学习率为0.0001。</p>
</li>
</ul>
<h2 id="3-SOC条件下分类结果"><a href="#3-SOC条件下分类结果" class="headerlink" title="3. SOC条件下分类结果"></a>3. SOC条件下分类结果</h2><ul>
<li>训练集和测试集如下：</li>
</ul>
<div class="table-container">
<table>
<thead>
<tr>
<th style="text-align:center">类别</th>
<th style="text-align:center">数量</th>
<th style="text-align:center"></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center"></td>
<td style="text-align:center">训练集17°</td>
<td style="text-align:center">测试集15°</td>
</tr>
<tr>
<td style="text-align:center">2S1</td>
<td style="text-align:center">299</td>
<td style="text-align:center">274</td>
</tr>
<tr>
<td style="text-align:center">BMP2</td>
<td style="text-align:center">233</td>
<td style="text-align:center">196</td>
</tr>
<tr>
<td style="text-align:center">BRDM2</td>
<td style="text-align:center">298</td>
<td style="text-align:center">274</td>
</tr>
<tr>
<td style="text-align:center">BTR60</td>
<td style="text-align:center">256</td>
<td style="text-align:center">195</td>
</tr>
<tr>
<td style="text-align:center">BTR70</td>
<td style="text-align:center">233</td>
<td style="text-align:center">196</td>
</tr>
<tr>
<td style="text-align:center">D7</td>
<td style="text-align:center">299</td>
<td style="text-align:center">274</td>
</tr>
<tr>
<td style="text-align:center">T62</td>
<td style="text-align:center">299</td>
<td style="text-align:center">273</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">232</td>
<td style="text-align:center">196</td>
</tr>
<tr>
<td style="text-align:center">ZIL131</td>
<td style="text-align:center">299</td>
<td style="text-align:center">274</td>
</tr>
<tr>
<td style="text-align:center">ZSU23/4</td>
<td style="text-align:center">299</td>
<td style="text-align:center">274</td>
</tr>
<tr>
<td style="text-align:center">总计</td>
<td style="text-align:center">2747</td>
<td style="text-align:center">2426</td>
</tr>
</tbody>
</table>
</div>
<ul>
<li><p>训练集每张SAR图像裁剪5个随机切片，中心裁剪1个切片，均为88×88像素，训练集一共2747×6=16482 (张)。测试集中心裁剪88×88的切片。</p>
</li>
<li><p>训练：epoch = 50， Adam优化器，learning_rate = 0.0001, 在tensorboard中显示训练集和测试集Accuracy、Loss曲线：</p>
<p><img src="https://img-blog.csdnimg.cn/3081fdc6e0f94e47be26a40e379289a5.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMTgxNTky,size_16,color_FFFFFF,t_70" alt="image-20210809103258789" style="zoom: 50%;" /><img src="https://img-blog.csdnimg.cn/c6e0887146e44b6688a3824935a227df.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMTgxNTky,size_16,color_FFFFFF,t_70" alt="image-20210809103401264" style="zoom: 50%;" /></p>
</li>
<li><p>计算测试集的混淆矩阵如下：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br></pre></td><td class="code"><pre><span class="line"> 	 2S1 BMP2 BRDM B60 B70 D7 T62 T72 ZIL ZSU	</span><br><span class="line">2S1[[263   0   0   0   0   0  11   0   0   0]	95.99%</span><br><span class="line">BMP2[  0 132   0  18   3   0   0  42   0   1]	67.35%</span><br><span class="line">BRDM[  0   0 272   0   0   0   0   0   0   2]	99.27%</span><br><span class="line">B60 [  0   0   0 192   0   0   0   0   1   2]	98.46%</span><br><span class="line">B70 [  0   0   0  13 181   0   0   2   0   0]	92.35%</span><br><span class="line">D7 	[  2   0   0   0   0 272   0   0   0   0]	99.27%</span><br><span class="line">T62 [  0   0   0   0   0   0 273   0   0   0]	100%</span><br><span class="line">T72 [  0   0   0   1   0   0   0 195   0   0]	99.49%</span><br><span class="line">ZIL [  0   0   0   0   0   4   0   0 270   0]	98.54%</span><br><span class="line">ZSU [  0   0   0   0   0   0   0   0   0 274]]	100%</span><br><span class="line">总计： 95.07% </span><br><span class="line">The Precision is :  0.9574354589252246</span><br><span class="line">The Recall is :  0.9507109053615832</span><br><span class="line">The Accuracy is :  0.9579554822753503</span><br><span class="line">The F1 is :  0.9493873742571258</span><br><span class="line">The F_beta is :  0.949223632328047</span><br><span class="line">The Auc Score is :  0.9996171502953114</span><br></pre></td></tr></table></figure>
</li>
</ul>
<h2 id="4-EOC1条件下分类结果"><a href="#4-EOC1条件下分类结果" class="headerlink" title="4. EOC1条件下分类结果"></a>4. EOC1条件下分类结果</h2><ul>
<li>EOC1是大俯仰角变化条件下，其训练集和测试集如下：</li>
</ul>
<div class="table-container">
<table>
<thead>
<tr>
<th style="text-align:center">类别</th>
<th style="text-align:center">数量</th>
<th style="text-align:center">数量</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center"></td>
<td style="text-align:center">训练集17°</td>
<td style="text-align:center">测试集30°</td>
</tr>
<tr>
<td style="text-align:center">2S1</td>
<td style="text-align:center">299</td>
<td style="text-align:center">288</td>
</tr>
<tr>
<td style="text-align:center">BRDM2</td>
<td style="text-align:center">298</td>
<td style="text-align:center">287</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">299</td>
<td style="text-align:center">288</td>
</tr>
<tr>
<td style="text-align:center">ZSU234</td>
<td style="text-align:center">299</td>
<td style="text-align:center">288</td>
</tr>
<tr>
<td style="text-align:center">总计</td>
<td style="text-align:center">1195</td>
<td style="text-align:center">1151</td>
</tr>
</tbody>
</table>
</div>
<ul>
<li><p>训练集每张SAR图像裁剪5个随机切片，中心裁剪1个切片，均为88×88像素，训练集一共1195×6=7170 (张)。测试集中心裁剪88×88的切片。</p>
</li>
<li><p>训练：epoch200，Adam优化器，learning_rate = 0.0001, 在tensorboard中显示训练集和测试集Accuracy、Loss曲线：</p>
<p><img src="https://img-blog.csdnimg.cn/b75af0e832194c55accb7cc102b0e762.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMTgxNTky,size_16,color_FFFFFF,t_70" alt="image-20210809120835524" style="zoom:50%;" /><img src="https://img-blog.csdnimg.cn/433d59ce8479447c9e49da304e75f7c0.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMTgxNTky,size_16,color_FFFFFF,t_70" alt="image-20210809120907698" style="zoom:50%;" /></p>
</li>
<li><p>计算测试集的混淆矩阵如下：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line">     2S1  BRDM  T72  ZSU	</span><br><span class="line">2S1   288   0    0    0		100%</span><br><span class="line">BRDM   0   287   0    0		100%</span><br><span class="line">T72    0    0   287   1		99.65%</span><br><span class="line">ZSU   59    0    0   229	79.51%</span><br><span class="line">总计： 94.79% </span><br><span class="line"></span><br><span class="line">The CNN Precision is :  0.9564058388673098</span><br><span class="line">The CNN Recall is :  0.9479166666666666</span><br><span class="line">The CNN Accuracy is :  0.947871416159861</span><br><span class="line">The CNN F1 is :  0.9473793419770824</span><br><span class="line">The CNN F_beta is :  0.9465925313694183</span><br></pre></td></tr></table></figure>
</li>
</ul>
<h2 id="5-EOC2条件下分类结果"><a href="#5-EOC2条件下分类结果" class="headerlink" title="5. EOC2条件下分类结果"></a>5. EOC2条件下分类结果</h2><ul>
<li>EOC2是车辆外观配置变化，其训练集如下：</li>
</ul>
<div class="table-container">
<table>
<thead>
<tr>
<th style="text-align:center">类别</th>
<th style="text-align:center">数量（俯仰角17°）</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">BMP2（9563）</td>
<td style="text-align:center">233</td>
</tr>
<tr>
<td style="text-align:center">BRDM2</td>
<td style="text-align:center">298</td>
</tr>
<tr>
<td style="text-align:center">BTR70（C71）</td>
<td style="text-align:center">233</td>
</tr>
<tr>
<td style="text-align:center">T72（132）</td>
<td style="text-align:center">232</td>
</tr>
<tr>
<td style="text-align:center">总计</td>
<td style="text-align:center">995</td>
</tr>
</tbody>
</table>
</div>
<ul>
<li>测试集：</li>
</ul>
<div class="table-container">
<table>
<thead>
<tr>
<th style="text-align:center">类别</th>
<th style="text-align:center">型号</th>
<th style="text-align:center">数量（俯仰角17° &amp; 15°）</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">S7</td>
<td style="text-align:center">419</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">A32</td>
<td style="text-align:center">572</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">A62</td>
<td style="text-align:center">573</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">A63</td>
<td style="text-align:center">573</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">A64</td>
<td style="text-align:center">573</td>
</tr>
<tr>
<td style="text-align:center">总计</td>
<td style="text-align:center"></td>
<td style="text-align:center">2710</td>
</tr>
</tbody>
</table>
</div>
<ul>
<li><p>训练集每张SAR图像裁剪10个随机切片，中心裁剪1个切片，均为88×88像素，训练集一共995×11=10945(张)。测试集中心裁剪88×88的切片。</p>
</li>
<li><p>训练：epoch50，Adam优化器，learning_rate = 0.0001, 在tensorboard中显示训练集和测试集Accuracy、Loss曲线：</p>
<p><img src="https://img-blog.csdnimg.cn/90a412f3ca334a4aaf9b3085a37f65c3.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMTgxNTky,size_16,color_FFFFFF,t_70" alt="image-20210809121909757" style="zoom:50%;" /><img src="https://img-blog.csdnimg.cn/a2c65823cd334b5a982bff9d3931732f.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMTgxNTky,size_16,color_FFFFFF,t_70" alt="image-20210809121932135" style="zoom:50%;" /></p>
</li>
<li><p>计算测试集的混淆矩阵如下：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">	BMP2  BRDM2  BTR70   T72  </span><br><span class="line">S7	 8		0	   0     411	98.09%</span><br><span class="line">A32	 0      0      0     572	100%</span><br><span class="line">A62  1      0      0     572	99.83%</span><br><span class="line">A63  1      0      0     572	99.83%</span><br><span class="line">A64  4      0      0     569	96.14%</span><br><span class="line">总计：98.78%</span><br></pre></td></tr></table></figure>
</li>
</ul>
<h2 id="6-EOC3条件下分类结果"><a href="#6-EOC3条件下分类结果" class="headerlink" title="6. EOC3条件下分类结果"></a>6. EOC3条件下分类结果</h2><ul>
<li>EOC3是针对同一目标的不同型号变种，训练集和EOC2训练集一致，测试集如下：</li>
</ul>
<div class="table-container">
<table>
<thead>
<tr>
<th style="text-align:center">类别</th>
<th style="text-align:center">型号</th>
<th style="text-align:center">数量（俯仰角17° &amp; 15°）</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">BMP2</td>
<td style="text-align:center">9566</td>
<td style="text-align:center">428</td>
</tr>
<tr>
<td style="text-align:center">BMP2</td>
<td style="text-align:center">C21</td>
<td style="text-align:center">429</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">812</td>
<td style="text-align:center">426</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">A04</td>
<td style="text-align:center">573</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">A05</td>
<td style="text-align:center">573</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">A07</td>
<td style="text-align:center">573</td>
</tr>
<tr>
<td style="text-align:center">T72</td>
<td style="text-align:center">A10</td>
<td style="text-align:center">567</td>
</tr>
<tr>
<td style="text-align:center">总计</td>
<td style="text-align:center"></td>
<td style="text-align:center">3569</td>
</tr>
</tbody>
</table>
</div>
<ul>
<li><p>这里直接用EOC2训练好的模型进行预测，测试集的混淆矩阵如下：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line">		   BMP2   BRDM2    BTR70   T72  </span><br><span class="line">BMP9566	   359		0		17	    52		83.88%</span><br><span class="line">BMPC21	   372		0		1		2		86.71%</span><br><span class="line">T72_812		2		0		1	   423		99.30%</span><br><span class="line">T72_A04		3		0		0	   570		99.48%</span><br><span class="line">T72_A05		1		0		0	   572		99.83%</span><br><span class="line">T72_A07		0		0		0	   573		100%</span><br><span class="line">T72_A10		20		0		0	   547		96.47%		</span><br><span class="line">总计：95.74%</span><br></pre></td></tr></table></figure>
</li>
</ul>

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